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High School Student with a Traditionally Underrepresented Background

HS Poster

Poster 26 - Assessing the Effect of Pain on Electroencephalogram Signals: a Sickle Cell Disease Study

Saturday, October 26, 2024
10:00 AM - 12:00 PM EST
Location: Exhibit Hall E, F & G

Introduction: Sickle Cell Disease (SCD) is an inherited disease that affects hemoglobin, causing red blood cells to sickle and increase incidence rates of blood vessel blockages, leading to impediments such as anemia, multiorgan impairments, and pain crises.1 Recent research explores the neurological impact on chronic and acute pain in patients with SCD and distinguishes SCD’s influence on nociceptive and neuropathic pain.1 However, current treatment and knowledge of the effects of chronic pain on SCD patients are limited. Emerging research focuses on GABAergic (gamma-aminobutyric acid) activity, a primary inhibitory neurotransmitter network that minimizes neuronal excitability and causes vasoconstriction, a reduction in blood flow and confinement of sickle cells in microvessels.2, 3 GABAergic neurons have a crucial role in producing high-frequency oscillations.4 An electroencephalogram (EEG) can, non-invasively, record electrical neurological activity on the scalp, which is favorable for conducting research on pain in patients with SCD.5 Since EEG can compute spectral power across the various frequencies on a time scale, its findings could be insightful for understanding nociceptive and neuropathic functions, especially at higher frequencies like beta and gamma waves. Prior studies show that SCD patients exhibit abnormal brain activity, including increased theta power and decreased beta power.4 The overall goals of this study are to assess the feasibility in investigating the variability of EEG spectral power between SCD patients and controls upon applying sensory pain, administered through quantitative sensory testing (QST), and to examine the relationship between pain intensity, pain sensitization, and blood circulation.

Materials and

Methods: We first investigated the feasibility of this study on 3 healthy adult male controls (aged 24 ± 1.414; 2 Black/African American, 1 Asian) to design our pipeline and analysis for the ongoing recruitment of participants. We collected EEG data using a BrainAmp MR and BrainCap (BRAIN PRODUCTS) and functional MRI data using 3T Siemens Prisma. Subjects completed 4 threshold tests: cold detection, warm detection, cold pain, and hot pain. Patients with SCD will also complete these tests. Baseline temperature was 32°C. The device’s safety limits were 0°C (cold pain) and 50.1°C (hot pain). EEG preprocessing was completed using the EEGLAB toolbox in MATLAB. MR artifact correction was completed using the standard pipeline on BrainVision Analyzer. The EEG data was filtered with a 1 Hz high-pass and 40 Hz low-pass filter to remove electrical noise. Inadequate channels were visually identified based on outliers on power spectral density (PSD) plots. Independent component analysis (ICA) was used to identify and remove unfavorable human artifacts (e.g. eye blinks, heartbeat, and muscle contractions).6 Channel spectra graphs were used to compare subjects for pain experiments. EEG power spectral analysis divided frequencies into delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-35 Hz), and gamma (35-100 Hz). Statistical parameters for plotting the average power spectrum containing all channels included permutation statistics (P < 0.05), FDR correction, and computed root mean square (RMS) power. Future analysis will include T-tests with power spectra and PSD between control and SCD patients.

Results, Conclusions, and Discussions: Results and Discussions: Across the 3 control subjects, electrical signals at beta and gamma waves were comparable (Figure 1). Final average temperature (in °C) per subject ranged between 27-30 for cold detection, 34-35 for warm detection, 0-6 for cold pain, and 46-49 for hot pain. We anticipate collecting EEG data from SCD patients (n=10) and more control subjects (n=7) in the future. All healthy controls consistently attained prominent peaks at beta (24 Hz) and gamma (36 Hz) frequency bands. Other notable peaks were discerned but not recurrent across all subjects. Based on prior studies, we expect patients with SCD to have neural frequency peaks at faster speeds, meaning higher frequency values on beta and gamma spectrums compared to controls.

Conclusions: Beta and gamma oscillations can be utilized as biomarkers for how the central nervous system is altered in SCD patients. Previous research indicates that beta and gamma waves signify GABAergic signaling.7 These high-frequency oscillations are correlated to brain areas responsible for pain, consisting of GABAergic neurons, and are correlated with subjective pain.8 Research also found that patients with SCD have a remarkably amplified vasoconstriction reaction to the anticipation of pain and thermal pain.3 Subsequently, we will investigate how power spectra vary with the temperature-related detection and pain tests on SCD patients as they are enrolled in this study. This can support constructing viable conclusions about high-frequency peaks linked with cognitive activity that may impact blood flow and perception of pain.

Acknowledgements (Optional): We would like to acknowledge Dr. Julia Xu and Dr. Christine Saber for helping us with the recruitment of patients and CMU-Pitt BRIDGE center staff for helping to scan the participants.

References (Optional): 1. Brandow AM. “Advances in the Diagnosis and Treatment of Sickle Cell Disease.” J Hematol Oncol, 2022, vol. 15, no. 1, pp. 20, https://doi.org/10.1186/s13045-022-01237-z
2. Allen MJ. “GABA Receptor.” 2024, https://www.ncbi.nlm.nih.gov/books/NBK526124/
3. Khaleel M, et al. “Individuals with Sickle Cell Disease Have a Significantly Greater Vasoconstriction Response to Thermal Pain Than Controls and Have Significant Vasoconstriction in Response to Anticipation of Pain.” American journal of hematology, 2017, vol. 92, no. 11, pp. 1137-1145, https://doi.org/10.1002/ajh.24858
4. Barr MS, et al. “Measuring GABAergic Inhibitory Activity with TMS-EEG and Its Potential Clinical Application for Chronic Pain.” J Neuroimmune Pharmacol, 2012, vol. 8, no. 3, pp. 535-546, https://doi.org/10.1007/s11481-012-9383-y
5. Case M, et al. “Increased Theta Band EEG Power in Sickle Cell Disease Patients.” J Pain Res, 2018, vol. 11, pp. 67-76, https://doi.org/10.2147/JPR.S145581
6. Delorme A. “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.” J Neurosci Methods, 2004, vol. 134, no. 1, pp. 9-21, https://doi.org/10.1016/j.jneumeth.2003.10.009
7. May ES, et al. “Prefrontal Gamma Oscillations Reflect Ongoing Pain Intensity in Chronic Back Pain Patients.” Hum Brain Mapp, 2019, vol. 40, no. 1, pp. 293-305, https://doi.org/10.1002/hbm.24373
8. Zolezzi DM. “EEG Frequency Band Analysis in Chronic Neuropathic Pain: A Linear and Nonlinear Approach to Classify Pain Severity.” Computer Methods and Programs in Biomedicine, 2023, vol. 230, pp. 107349, https://doi.org/10.1016/j.cmpb.2023.107349